A fast direct locator for radiation source based on composite convolution neural network

The high spatial search complexity of the direct positioning method in passive positioning systems leads to long positioning time and high computational resource consumption. In response to this issue, this article proposes a fast localization scheme based on composite convolutional neural networks (CCNN), which can effectively explore the correlation between the position of the radiation source and the characteristics of the received signal. CCNN is a 20‐layer composite network based on fully convolutional network layer, which is composed of convolutional layers, batch normalization (BN) layers, and ReLU activation function layers with unidirectional connections. Then, CCNNs are adjusted and trained for positioning single and multiple radiation sources, respectively. Simulation results show that the computational time of the proposed method can be reduced by nearly 98% compared with the direct positioning scheme. Meanwhile, about 71.2% of positioning error's reduction is achieved. In this article, we propose a fast source localization algorithm based on composite convolutional neural network. Our network is less complex than direct positioning scheme, but can achieve better positioning performance. It can also be easily extended to the field of multi‐source localization, which also has good robustness to small‐scale fading of wireless channel.